Evidence map›Paper›PMID 41923938›Full record

ReviewFrontiers in plant science2026

Nano-enabled plant genetic engineering for stress resilience: current advances and future directions.

Salem M Al-Amri

Abstract readReview
In one paragraph

Review in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Salem M Al-AmriCollege of Science and Humanities, Department of Biology, Shaqra University, Dawadmi, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plant productivity and food security are increasingly threatened by abiotic and biotic stresses intensified by climate change. Plant genetic engineering offers powerful solutions to enhance stress resilience; however, conventional transformation approaches remain constrained by limited host range, low precision, tissue culture dependency, and regulatory concerns. In this context, nanotechnology has emerged as a transformative enabling platform for precise, efficient, and species-independent delivery of genetic cargo into plant systems. This review provides a comprehensive overview of recent advances in nano-enabled plant genetic engineering for stress resilience, highlighting the role of diverse nanocarriers, including carbon-based nanomaterials (NMs), metal and metal oxide nanoparticles (NPs), polymer-based nanocarriers, and metal-organic frameworks in delivering DNA, RNA interference constructs, and genome-editing components. These nanoplatforms overcome key biological barriers, protect nucleic acids from degradation, and enable controlled, targeted, and often transgene-free genetic modulation. Beyond delivery, many NMs exhibit intrinsic bioactivity, which can synergistically enhance plant stress tolerance through redox regulation, nutrient supplementation, and activation of stress-responsive pathways. The review also critically discusses regulatory and biosafety challenges associated with nano-enabled delivery systems, emphasizing the need for harmonized frameworks tailored to NMs-specific properties. Finally, future perspectives are outlined, focusing on biodegradable nanocarriers, organelle-specific targeting, and integration with CRISPR-based technologies to advance sustainable, precise, and climate-resilient crop improvement strategies.

Indexed as

gene deliveryplant genetic engineeringRNA interferencesmart nanocarriersstress resilience

Identifiers

PMID41923938
PMCPMC13035748

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.